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Towards optimal head-to-head autonomous racing with curriculum reinforcement learning
Head-to-head autonomous racing is a challenging problem, as the vehicle needs to operate
at the friction or handling limits in order to achieve minimum lap times while also actively …
at the friction or handling limits in order to achieve minimum lap times while also actively …
Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute
Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as State-of-the-
Art (SotA) modelbased techniques rely on precise knowledge of the vehicle's parameters …
Art (SotA) modelbased techniques rely on precise knowledge of the vehicle's parameters …
Autonomous Drifting Based on Maximal Safety Probability Learning
This paper proposes a novel learning-based framework for autonomous driving based on
the concept of maximal safety probability. Efficient learning requires rewards that are …
the concept of maximal safety probability. Efficient learning requires rewards that are …
Real-Time Algorithms for Game-Theoretic Motion Planning and Control in Autonomous Racing using Near-Potential Function
Autonomous racing extends beyond the challenge of controlling a racecar at its physical
limits. Professional racers employ strategic maneuvers to outwit other competing opponents …
limits. Professional racers employ strategic maneuvers to outwit other competing opponents …
Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning
Reinforcement learning (RL) agents need to explore their environment to learn optimal
behaviors and achieve maximum rewards. However, exploration can be risky when training …
behaviors and achieve maximum rewards. However, exploration can be risky when training …